Plant-part and environmental gradients shape plant microbiomes
C Wall, J Bernard, and UH Mānoa BOT662, submitted ISME J
2/10/2020
Overview
Plant part and environmental context shape symbiotic microbial compositions, yet the interaction of these two variables are seldom considered. We analyzed epiphytic microbes from nine Hibiscus tiliaceus (Hawaiian: hau) trees across a steep environmental gradient within a single Hawaiian watershed in Waimea Valley on the north shore of O‘ahu, Hawai‘i. At each location we sampled 8 microhabitats: leaves, petioles, axils, stems, roots, and litter from the plant, as well as surrounding air and soil.
The composition of microbial communities is driven primarily by microhabitat (i.e., above vs. below ground communities), this variable predicted more than twice the compositional variance in bacteria compared to fungi. Fungal community compositions, in contrast, were more responsive to gradient dynamics than bacteria, and there were differences between spatial dynamics of fungal communities associated with different microhabitats that correlate with distribution patterns and range size. Within plants, microbes were compositionally nested with aboveground communities containing a subset of diversity found belowground. Our findings suggest potential differences in the underlying mechanisms shaping communities of fungi and bacteria associated with plants, and indicate an interaction between assembly mechanisms working simultaneously on different spatial scales.
if (!require('knitr')) install.packages('knitr'); library('knitr')
knitr::opts_chunk$set(warning=FALSE, message=FALSE, fig.align='center')
# Load in packages
if (!require("pacman")) install.packages("pacman"); library(pacman) # for rapid install if not in library
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager") #Load packages from Bioconductor
pacman::p_load('FedData', 'latticeExtra', 'scales', 'viridis', 'ggmap', 'phyloseq', 'raster', 'rgdal', 'RColorBrewer', 'ggplot2', 'vegan', 'ggplot2', 'phyloseq', 'bipartite', 'plotrix', 'viridis', 'lattice', 'fossil', 'plyr', 'devtools', 'ggpubr', 'gridExtra', 'cowplot')
devtools::install_bitbucket("graumannlabtools/multipanelfigure")Site Map
The site where the study was conducted: Waimea Valley on the north shore of O‘ahu, Hawai‘i.
# Code by Austin Greene
# Edited by Chris Wall
# Purpose: Generate a map of Waimea Valley, Oahu with elevation data,
# and site locations which are scaled the mean-annual precipitation
# Load in physeq object
physeq1=readRDS("data/physeq1.gz")
# Getting base map via Stamen of location within bounding box.
# Using Stamen maps which do not require a Google API key
map3 <- get_map(location = c(left = -158.070631, bottom = 21.598356, right = -157.998464, top = 21.655101), zoom=13, source="stamen", maptype = c("terrain-background"))
Waimea_map_3 <- ggmap(map3) # Turn into ggmap graphical object
# Generating extents of map to pull elevation
bb <- attr(map3, "bb") # Mapp attribute object, from which we extract map extents
extentB <- polygon_from_extent(raster::extent(bb$ll.lon, bb$ur.lon, bb$ll.lat, bb$ur.lat),
proj4string = "+proj=longlat +ellps=GRS80 +datum=NAD83 +no_defs")
# Confirm bounding box for our elevation data
# ggmap(map3) + geom_polygon(data=extentB, fill=NA, aes(x=long,y=lat), color="red", size=3)
# Download elevation data tiles
elev_waimea <- get_ned(template = extentB, label = "ned_waimea", res="1", force.redo = F)
# Make df of elevation data with lat and long included
elev_raster_df <- raster::as.data.frame(elev_waimea, xy=TRUE) #xy True
# Make a dataframe of geographic location and mean annual precip at each site (from existing metadata)
geo_p=cbind(sample_data(physeq1)$Long, sample_data(physeq1)$Lat, sample_data(physeq1)$rain, sample_data(physeq1)$FieldSite)
geo3 = data.frame(geo_p) #we convert it to a dataframe
colnames(geo3)=c("Long", "Lat","Rain", "Site") # Repair column names
geo3 <- subset(geo3, Site != 9) # Remove Site 9
geo3$Site <- factor(geo3$Site) # Convert sites to factors, not critical
# Plot elevational data and site locations scaled by precipitation on top of existing ggmap of Waimea Valley
Waimea_map_6_sizescaled <- Waimea_map_3 + # Existing map
geom_raster(data = elev_raster_df, aes(x=x, y=y, fill=elev_raster_df$ned_waimea_NED_1)) + # Raster elevation data
geom_point(data = geo3, aes(x=Long, y=Lat, size=Rain), fill="white", color="black", pch=21, alpha=0.5) + # Points for sites, scaled by mean-annual precipitation
coord_cartesian() + # Set to cartesian coordinates
scale_fill_viridis(option = "plasma", alpha = 0.7) + # Set fill colors to be colorblind-friendly
coord_fixed(1.3) + # Aspect ratio of cartesian coordinates is 1.3
labs(x="Longitude", y="Latitude", size="Precipitation (mm/year)", fill="Elevation (m)") + # Labels
theme_classic() # Classic minimalist theme
Waimea_map_6_sizescaled # Print the plot